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Record W3099939759 · doi:10.1016/j.jtemb.2020.126682

Blood cadmium levels as a marker for early lung cancer detection

2020· article· en· W3099939759 on OpenAlexaff
Marcin Lener, Edyta Reszka, Wojciech Marciniak, Monika Lesicka, Piotr Baszuk, Ewa Jabłońska, Katarzyna Białkowska, Magdalena Muszyńska, Sandra Pietrzak, Róża Derkacz, Tomasz Grodzki, Janusz Wójcik, Małgorzata Wojtyś, Tadeusz Dębniak, Cezary Cybulski, Jacek Gronwald, Bartosz Kubisa, Jarosław Pieróg, Piotr Waloszczyk, Rodney J. Scott, Anna Jakubowska, Steven A. Narod, Jan Lubiński

Bibliographic record

VenueJournal of Trace Elements in Medicine and Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsWomen's College Hospital
FundersNarodowe Centrum Badań i Rozwoju
KeywordsLung cancerMedicineQuartileOdds ratioInternal medicineCancerContext (archaeology)CadmiumStage (stratigraphy)LungCase-control studyGastroenterologyConfidence intervalBiology

Abstract

fetched live from OpenAlex

BACKGROUND: We assessed whether blood cadmium levels were associated with incident lung cancer and could be used in the context of a screening program for early-stage lung cancer. MATERIAL AND METHODS: We measured blood cadmium levels among 205 lung cancer patients and 205 matched controls. Cases and controls were matched for sex, age and smoking history (total pack-years, years since cessation for former smokers). RESULTS: The odds ratio for those in the highest quartile of cadmium level (versus lowest) was four-fold (OR = 4.41, 95 % CI:2.01-9.67, p < 0.01). The association was present in former smokers (OR = 16.8, 95 % CI:3.96-71.2, p < 0.01), but not in current smokers (OR = 1.23, 95 % CI: 0.34-4.38) or in never smokers (OR not defined). Among former smokers, the association was present in both early- and late-stage lung cancer. CONCLUSION: Blood cadmium levels may be a marker to help with the early detection of lung cancer among former smokers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.351
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations54
Published2020
Admission routes1
Has abstractyes

Explore more

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